The U.S. Pat. No. 5,999,355 describes an asynchronous receiver such as the one mentioned in the opening paragraph. In accordance with the cited patent, the equalizer is a tapped delay line (Finite Impulse Response filter) with a tap spacing of Ts seconds, and control of the equalizer is based on the classical LMS (Least Mean Square) algorithm. That is to say, updates of the equalizer tap values are produced by correlating the tap sequences with a suitable error sequence. Classical LMS techniques normally apply to synchronous receivers wherein error and tap sequences have the same sampling rate and are phase synchronous. The asynchronous receiver described in the cited patent thus comprises at least two provisions in order that error and tap sequences have the same sampling rate and are phase synchronous. The latter condition implies that any latency in the error sequence should be matched by delaying the tap sequences accordingly. The aforementioned two provisions include an inverse sampling rate conversion (ISRC) for converting the synchronous error sequence at the data rate 1/T into an equivalent error sequence of sampling rate 1/Ts, and delay means to provide delayed versions of the equalizer tap sequences to match the “round-trip” delay arising in the formation of the equivalent error sequence from the equalizer output. This “round-trip” delay is not accurately known a priori because both SRC and inverse SRC introduce a time-varying delay. The matching delay represents the expected or average value of the “round-trip” delay. Discrepancies between the “round-trip” and matching delays tend to cause the adaptation scheme to converge to an erroneous solution. Furthermore, since the matching delay needs not be an integer number of symbol intervals Ts, implementation of the matching delay may require some form of interpolation. This adds to the complexity of the system. The inverse SRC also adds to this complexity so that overall complexity of the adaptation-related circuitry is considerably larger than in synchronous LMS-based adaptation.